
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE				:str									=							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE				:List[str]									=							'▁'

SCREAMING_SNAKE_CASE				:int									=							{
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
}

SCREAMING_SNAKE_CASE				:Optional[Any]									=							{
    'vocab_file': {
        'facebook/s2t-small-librispeech-asr': (
            'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'
        ),
    },
    'spm_file': {
        'facebook/s2t-small-librispeech-asr': (
            'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'
        )
    },
}

SCREAMING_SNAKE_CASE				:Any									=							{
    'facebook/s2t-small-librispeech-asr': 1024,
}

SCREAMING_SNAKE_CASE				:List[Any]									=							['pt', 'fr', 'ru', 'nl', 'ro', 'it', 'es', 'de']

SCREAMING_SNAKE_CASE				:Optional[Any]									=							{'mustc': MUSTC_LANGS}





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





	snake_case_          =	VOCAB_FILES_NAMES
	snake_case_          =	PRETRAINED_VOCAB_FILES_MAP
	snake_case_          =	MAX_MODEL_INPUT_SIZES
	snake_case_          =	["input_ids", "attention_mask"]

	snake_case_          =	[]






	def __init__(			self   :					Optional[Any]				,A   :					int				,A   :					Dict				,A   :					int="<s>"				,A   :					List[Any]="</s>"				,A   :					Dict="<pad>"				,A   :					List[str]="<unk>"				,A   :					List[Any]=False				,A   :					List[str]=False				,A   :					Union[str, Any]=None				,A   :					Dict=None				,A   :					Optional[Dict[str, Any]] = None				,**A   :					Optional[int]				,):
					__A       =					{} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    bos_token=A				,eos_token=A				,unk_token=A				,pad_token=A				,do_upper_case=A				,do_lower_case=A				,tgt_lang=A				,lang_codes=A				,sp_model_kwargs=self.sp_model_kwargs				,**A				,)
					__A       =					do_upper_case
					__A       =					do_lower_case

					__A       =					load_json(A					)
					__A       =					{v: k for k, v in self.encoder.items()}
					__A       =					spm_file
					__A       =					load_spm(A				,self.sp_model_kwargs					)

					if lang_codes is not None:
									__A       =					lang_codes
									__A       =					LANGUAGES[lang_codes]
									__A       =					[f'''<lang:{lang}>''' for lang in self.langs]
									__A       =					{lang: self.sp_model.PieceToId(f'''<lang:{lang}>'''					) for lang in self.langs}

									__A       =					self.lang_tokens
									__A       =					tgt_lang if tgt_lang is not None else self.langs[0]

									self.set_tgt_lang_special_tokens(self._tgt_lang					)
					else:
									__A       =					{}






	@property
	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					return len(self.encoder					)






	@property
	def 							UpperCamelCase_    (			self   :					List[Any]					):
					return self._tgt_lang






	@tgt_lang.setter
	def 							UpperCamelCase_    (			self   :					str				,A   :					Any					):
					__A       =					new_tgt_lang
					self.set_tgt_lang_special_tokens(A					)






	def 							UpperCamelCase_    (			self   :					List[str]				,A   :					str					):
					__A       =					self.lang_code_to_id[tgt_lang]
					__A       =					[lang_code_id]






	def 							UpperCamelCase_    (			self   :					Dict				,A   :					str					):
					return self.sp_model.encode(A				,out_type=A					)






	def 							UpperCamelCase_    (			self   :					List[str]				,A   :					Tuple					):
					return self.encoder.get(A				,self.encoder[self.unk_token]					)






	def 							UpperCamelCase_    (			self   :					Union[str, Any]				,A   :					int					):
					return self.decoder.get(A				,self.unk_token					)






	def 							UpperCamelCase_    (			self   :					Tuple				,A   :					List[str]					):
					__A       =					[]
					__A       =					""
					for token in tokens:
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
													__A       =					self.sp_model.decode(A					)
													out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
													__A       =					[]
									else:
													current_sub_tokens.append(A					)
					__A       =					self.sp_model.decode(A					)
					out_string += decoded.upper() if self.do_upper_case else decoded
					return out_string.strip()






	def 							UpperCamelCase_    (			self   :					Optional[int]				,A   :					int				,A   :					Union[str, Any]=None					):
					if token_ids_a is None:
									return self.prefix_tokens + token_ids_a + [self.eos_token_id]
					# We don't expect to process pairs, but leave the pair logic for API consistency
					return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]






	def 							UpperCamelCase_    (			self   :					Dict				,A   :					List[int]				,A   :					Optional[List[int]] = None				,A   :					bool = False					):

					if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=A				,token_ids_a=A				,already_has_special_tokens=A					)

					__A       =					[1] * len(self.prefix_tokens					)
					__A       =					[1]
					if token_ids_a is None:
									return prefix_ones + ([0] * len(A					)) + suffix_ones
					return prefix_ones + ([0] * len(A					)) + ([0] * len(A					)) + suffix_ones






	def 							UpperCamelCase_    (			self   :					List[Any]					):
					__A       =					self.encoder.copy()
					vocab.update(self.added_tokens_encoder					)
					return vocab






	def __getstate__(			self   :					Optional[Any]					):
					__A       =					self.__dict__.copy()
					__A       =					None
					return state






	def __setstate__(			self   :					Optional[int]				,A   :					Dict					):
					__A       =					d

					# for backward compatibility
					if not hasattr(self				,"sp_model_kwargs"					):
									__A       =					{}

					__A       =					load_spm(self.spm_file				,self.sp_model_kwargs					)







	def 							UpperCamelCase_    (			self   :					Union[str, Any]				,A   :					str				,A   :					Optional[str] = None					):
					__A       =					Path(A					)
					assert save_dir.is_dir(), f'''{save_directory} should be a directory'''
					__A       =					save_dir / (
					    (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
					)
					__A       =					save_dir / (
					    (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
					)

					save_json(self.encoder				,A					)

					if os.path.abspath(self.spm_file					) != os.path.abspath(A					) and os.path.isfile(self.spm_file					):
									copyfile(self.spm_file				,A					)
					elif not os.path.isfile(self.spm_file					):
									with open(A				,"wb"					) as fi:
													__A       =					self.sp_model.serialized_model_proto()
													fi.write(A					)

					return (str(A					), str(A					))





def 		UpperCAmelCase     (      a_						,  a_		)						->      sentencepiece.SentencePieceProcessor:







				"""simple docstring"""



				__A       =					sentencepiece.SentencePieceProcessor(**a_		)
				spm.Load(str(a_		)		)
				return spm


def 		UpperCAmelCase     (      a_		)						->      Union[Dict, List]:







				"""simple docstring"""



				with open(a_						,  "r"		) as f:
								return json.load(a_		)


def 		UpperCAmelCase     (      a_						,  a_		)						->      None:







				"""simple docstring"""



				with open(a_						,  "w"		) as f:
								json.dump(a_						,  a_						,  indent=2		)


import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
		import tensorflow as tf

if is_tensorflow_text_available():
		from transformers.models.bert import TFBertTokenizer


lowercase    :   Union[str, Any]						=			['bert-base-uncased', 'bert-base-cased']
lowercase    :   Any						=			'hf-internal-testing/tiny-bert-tf-only'

if is_tf_available():




		class 					lowerCamelCase__				(							tf.keras.Model):




			'''simple docstring'''




			def __init__(   self						:List[Any]	,  a						:int		)						->					int:
										super().__init__()
										__UpperCamelCase      :  Any       =   tokenizer
										__UpperCamelCase      :  Union[str, Any]       =   AutoConfig.from_pretrained(a		)
										__UpperCamelCase      :  Dict       =   TFAutoModel.from_config(a		)







			def   _lowerCamelCase							(   self						:List[Any]	,  a						:Dict		)						->					Any:
										__UpperCamelCase      :  int       =   self.tokenizer(a		)
										__UpperCamelCase      :  int       =   self.bert(**a		)
										return out["pooler_output"]




@require_tf
@require_tensorflow_text
class 					lowerCamelCase__				(							unittest.TestCase):




	'''simple docstring'''




	def   _lowerCamelCase							(   self						:str		)						->					str:
								super().setUp()

								__UpperCamelCase      :  Dict       =   [
								    BertTokenizer.from_pretrained(a		) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
								]  # repeat for when fast_bert_tokenizer=false
								__UpperCamelCase      :  List[Any]       =   [TFBertTokenizer.from_pretrained(a		) for checkpoint in TOKENIZER_CHECKPOINTS] + [
								    TFBertTokenizer.from_pretrained(a	,  use_fast_bert_tokenizer=a		)
								    for checkpoint in TOKENIZER_CHECKPOINTS
								]
								assert len(self.tokenizers		) == len(self.tf_tokenizers		)

								__UpperCamelCase      :  Dict       =   [
								    "This is a straightforward English test sentence.",
								    "This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.",
								    "Now we're going to add some Chinese: 一 二 三 一二三",
								    "And some much more rare Chinese: 齉 堃 齉堃",
								    "Je vais aussi écrire en français pour tester les accents",
								    "Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
								]
								__UpperCamelCase      :  Dict       =   list(zip(self.test_sentences	,  self.test_sentences[::-1]		)		)







	def   _lowerCamelCase							(   self						:List[Any]		)						->					int:
								for tokenizer, tf_tokenizer in zip(self.tokenizers	,  self.tf_tokenizers		):
															for test_inputs in (self.test_sentences, self.paired_sentences):
																						__UpperCamelCase      :  str       =   tokenizer(a	,  return_tensors="tf"	,  padding="longest"		)
																						__UpperCamelCase      :  Any       =   tf_tokenizer(a		)

																						for key in python_outputs.keys():
																													self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape		)		)
																													self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]	,  tf.intaa		) == tf_outputs[key]		)		)







	@slow
	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					Any:
								for tf_tokenizer in self.tf_tokenizers:
															__UpperCamelCase      :  List[str]       =   tf_tokenizer(self.paired_sentences		)
															__UpperCamelCase      :  Union[str, Any]       =   tf_tokenizer(
															    text=[sentence[0] for sentence in self.paired_sentences]	,  text_pair=[sentence[1] for sentence in self.paired_sentences]	,  )
															for key in merged_outputs.keys():
																						self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]	,  tf.intaa		) == separated_outputs[key]		)		)







	@slow
	def   _lowerCamelCase							(   self						:Any		)						->					Optional[int]:
								for tf_tokenizer in self.tf_tokenizers:
															__UpperCamelCase      :  Optional[Any]       =   tf.function(a		)
															for test_inputs in (self.test_sentences, self.paired_sentences):
																						__UpperCamelCase      :  Tuple       =   tf.constant(a		)
																						__UpperCamelCase      :  List[str]       =   compiled_tokenizer(a		)
																						__UpperCamelCase      :  Any       =   tf_tokenizer(a		)

																						for key in eager_outputs.keys():
																													self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]		)		)







	@slow
	def   _lowerCamelCase							(   self						:List[Any]		)						->					Tuple:
								for tf_tokenizer in self.tf_tokenizers:
															__UpperCamelCase      :  str       =   ModelToSave(tokenizer=a		)
															__UpperCamelCase      :  List[Any]       =   tf.convert_to_tensor(self.test_sentences		)
															__UpperCamelCase      :  Optional[int]       =   model(a		)  # Build model with some sample inputs
															with TemporaryDirectory() as tempdir:
																						__UpperCamelCase      :  Optional[Any]       =   Path(a		) / "saved.model"
																						model.save(a		)
																						__UpperCamelCase      :  List[Any]       =   tf.keras.models.load_model(a		)
															__UpperCamelCase      :  Optional[Any]       =   loaded_model(a		)
															# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
															self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output		)		)	,  1E-5		)